Thesis - "Deep Representation Learning of Mitochondrial Dynamics"
R. Mattson.
[Submitted]
Dissertation - "Investigating ACE2 Localization in Respiratory Cilia and Characterizing Cilia Motion Phenotypes"
C. Li.
[Submitted]
Thesis - "Cilia Segmentation Using U-Net with Gabor Filter"
C. Li.
[galib]
[🔒 proquest]
Thesis - "Incorporating task-agnostic information in task-based active learning using a variational autoencoder"
C. Godwin.
[galib]
[🔒 proquest]
Poster - "Variational Autoencoders for semi-supervised deep metric learning" SciPy 2022.
N. Safir, M. Zain, C. Godwin, E. Miller, I. Humphrey, and S. Quinn.
[poster]
Thesis - "Variational Autoencoders for semi-supervised deep metric learning"
N. Safir.
[galib]
Dissertation - "Modeling and analysis of mitochondrial dynamics using dynamic social network graphs"
M. Hill.
[galib]
Dissertation - "SUBMiT: Scalable Unsupervised Biomedical Motion Tracking"
M. Fazli.
[galib]
[proquest]
Thesis - "Graph-based classification of mitochondrial morphologies"
N. Pulagam.
[galib]
Poster - "Merging Metric Learning with Variational Autoencoders for Representation Learning of Semi-Supervised Data." CURO Symposium, University of Georgia.
N. Safir and S. Quinn.
[poster]
Poster - "Deep Clustering Methods for Subtyping Parkinson’s Disease." CURO Symposium, University of Georgia.
H. Yadav and S. Quinn.
[poster]
Poster - "Biosurveillance and Disease Monitoring through Temporal Word Embeddings of Tweets." CURO Symposium, University of Georgia.
I. Mo and S. Quinn.
[poster]
Poster - "Parkinson's Disease Biomarkers: A Deep Learning Based Neuroimage Analysis." CURO Symposium, University of Georgia.
H. Yadav and S. Quinn.
[poster]
Poster - "An Unsupervised Approach to Characterizing Ciliary Motion." CURO Symposium, University of Georgia.
N. Safir and S. Quinn.
[poster]
Poster - "Unsupervised Spatiotemporal Representation of Cilia Video Using A Modular Generative Pipeline." CURO Symposium, University of Georgia.
M. Zain and S. Quinn.
[poster]
Poster - "Unsupervised Spatiotemporal Representation of Cilia Video Using A Modular Generative Pipeline." Southern Data Science Conference (SDSC 2020), Atlanta, GA.
M. Zain, S. Rao, N. Safir, Q. Wyner, I. Humphrey, A. Eldridge, C. Li, B. AlAila, and S. Quinn.
[poster]
Thesis - "Reproducibility in A.I. for biomedical research: An application to Parkinson's Disease"
C. McDaniel.
[galib]
Thesis - "Cilia Segmentation in Medical Videos with Fourier Convolutional Neural Network"
W. Xu.
[pdf]
[proquest]
Invited Presentation - "CiliaWeb: A quantitative measure of ciliary motion for translational biomedicine." Department of Developmental Biology Seminar, University of Pittsburgh School of Medicine.
[slides]
Poster - "Teaching ethical data science through real-world re-enactment." CTL Faculty Teaching Celebration and Luncheon, Georgia Museum of Art, Athens, GA.
[poster]
Invited Presentation - "Parameterizing Spatiotemporal Dynamics of Biological Systems with Images (Or: How I Learned to Relax and Love Visual Change)." Department of Infectious Diseases Seminar, University of Georgia.
[slides]
Thesis - "Modeling Diffuse Subcellular Protein Structures as Dynamic Social Networks"
A. Durden.
[preprint] [slides]
Poster - "Scalable 3D Tracking and Clustering of Toxoplasma gondii Motion Phenotypes." Southern Big Data Hub All Hands, Atlanta, GA.
[poster]
Dissertation - "Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning"
R. Ceren.
[preprint]
Poster - "Using SpatialDE to Characterize Spatiotemporal Changes in Mitochondrial Morphology." UGA Population Biology of Infectious Diseases Research Experience for Undergraduates (REU), Athens, GA.
B. Dorsey, A. Durden,, A. Loy, B. Reaves, M. Fazli, F. D. Quinn, C. Chennubhotla, and S. Quinn.
[poster] [github]
Poster - "Dynamic Social Network Modeling of Diffuse Subcellular Morphologies." Southern Data Science Conference, Atlanta, GA.
[poster]
Poster - "Stacked Neural Networks for Automated Ciliary Motion Analysis." IEEE ISBI. Washington, D.C.
C. Lu, M. Marx, C. Chennubhotla, M. Zahid, C. W. Lo, and S. Quinn.
[preprint] [poster]
Poster - "Modeling the Motion of Toxoplasma gondii." Southern Data Science Conference, Atlanta, GA.
[poster]
Invited Presentation - "Reproducible Segmentation of Not-Quite-Objects in Jupyter Notebooks." Jupyter Day, Atlanta, GA.
[github]
Invited Presentation - "UGA ACM Faculty Spotlight Talk: Python for Biomedical Imaging." UGA ACM, University of Georgia.
[github]
Poster - "Calcium Signaling and Motility of Toxoplasma gondii." São Paulo School for Advanced Science in Cell Biology, São Paulo, Brazil.
[poster]
Thesis - "Generative spatiotemporal modeling of neutrophil behavior"
N. Pandhe.
[galib]
Thesis - "Open source distributed spectral clustering"
A. Joshi.
[galib]
Thesis - "Towards ciliary motion subtyping: representing patients as mixture of motion patterns"
A. Chennupati.
[galib]
Invited Presentation - "Skill and Readiness of the Technical Informatics Workforce." Advancing Informatics in Government and Industry, University of Georgia.
[slides]
Poster - "Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii." KDD Bigdas Workshop 2017, Halifax, Canada.
[poster]
Invited Presentation - "Python for Public Health: Case Studies in Bioimaging." PyData ATL Meetup, Atlanta, GA.
[slides]
Poster - "Calcium Signaling and Motility of Toxoplasma gondii." The 14th Toxoplasma gondii Research Community Biennial Meeting, Tomar, Portugal.
[poster]
Poster - "Developing motion models of Toxoplasma gondii." Computer Science Research Day, University of Georgia.
[poster]
Invited Presentation - "Python for Public Health: Case Studies in Bioimaging." Southern Data Science Conference, Atlanta, GA.
[slides]
Invited Presentation - "UGA ACM: Machine Learning Workshop." UGA ACM, University of Georgia.
[github]
Poster - "Identification of Vaccine Misinformation Online." CURO Symposium, University of Georgia.
J. Waring and S. Quinn.
[poster] [github]
Poster - "Large-scale Analysis of Spatiotemporal Organellar Network Evolution." Quantitative Bioimaging Conference, Texas A&M.
[poster] [abstract]
Thesis - "Tracking and quantifying negative content on social media"
M. Ranjan.
[galib]
Thesis - "Large scale semi-supervised learning"
M. Gadgil.
[galib]
Poster - "Data Science for Public Health." Georgia Informatics Symposium, University of Georgia.
[poster]
Poster - "Ca2+ Signaling and Motility in Toxoplasma gondii." Molecular Parasitology Meeting, Marine Biological Laboratory.
[poster] [abstracts]
Poster - "Mining Pre-Exposure Prophylaxis Trends in Social Media." Southern Translational Education and Research (STaR) Conference, University of Georgia.
[pdf]
Presentation - "Python for Public Health: Building Statistical Models of Ciliary Motion." PyCon 2016, Portland, OR.
[slides] [video]
Presentation - "Dr. Twitter: The Logistics of Practical Disease Surveillance with Social Media." International Conference on Biomedical and Health Informatics (BHI), IEEE EMBS, Las Vegas, NV.
Presentation - "Scaling Unsupervised Ciliary Motion Analysis for Actionable Biomedical Insights with PySpark." Spark Summit East, New York, NY.
[slides] [video]
Poster - "Unsupervised discovery and definition of latent ciliary beat patterns." IOB Big Data Challenges in Life Sciences, University of Georgia.
[pptx]
Presentation - "Towards a Quantitative Definition of Ciliary Motion Phenotypes." Southern Translational and Education Research (STaR) Conference, University of Georgia.
[pptx]
Invited Presentation - "Distributed Spectral Graph Methods for Analyzing Large-Scale Unstructured Biomedical Data." Institute of Bioinformatics Seminar Series, University of Georgia.
Invited Presentation - "Advanced Computing Infrastructure for Biomedical Imaging." ORNL Biomedical Science and Engineering Conference, Oak Ridge National Laboratory.
[materials]
Presentation - "Distributed Spectral Graph Methods for Analyzing Large-Scale Unstructured Biomedical Data." Ph.D. Thesis Defense, University of Pittsburgh.
[pptx]
Presentation - "Dr. Mahout: Analyzing clinical data using scalable and distributed computing." ApacheCon NA, Vancouver, BC, Canada.
[pptx]


| HOME | WHAT, WHEN & WHERE | FIXTURES & RESULTS | FAQ'S | NEWS | SOCIAL EVENTS | FEATURES | USEFUL LINKS |
Welcome to
BH Pegasus Badminton Club!
We play in the Basingstoke & District Badminton League (BDBL)
| Website updated: 24.08.14 | Email us at<EMAIL_ADDRESS>| Follow us on Facebook: http://www.facebook.com/bhpegasus |
| BH Pegasus Badminton Club, The Sports Centre, Festival Place, Basingstoke, Hampshire RG21 7LE Tel: 01256 326331 |


Algorithm and scientific questions: <Huidong.Chen at mgh dot harvard dot edu>
Module wrapping issues: Ted Liefeld < jliefeld at cloud dot ucsd dot edu>
STREAM (Single-cell Trajectories Reconstruction, Exploration And Mapping) is an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. Within GenePattern STREAM is implemented as a collection of modules that cover the entire STREAM processing pipeline to allow individual steps to be performed interactively for data exploration.
STREAM.DetectLabelSpecificMarkers is used to detect cell population-specific markers. This is similar to leaf branch marker gene detection. Here instead of comparing the gene expression values between leaf branches, we compare the gene expression values between cell populations.
For each gene E we scale the gene expression values to [0,1]. Then we calculate the average gene expressions for all cell populations. Based on the average expressions, we calculate the Z-scores of all populations. If there is any population with an absolute Z-score greater than 1.5, then the population with the highest absolute Z-score value will be picked as the candidate cell population. Next, Kruskal–Wallis H-test is computed for all the cell populations to test if a significant difference of gene expression median value between cell populations exists. If it is significant (p-value < 0.01), then a post-hoc pairwise Conover’s test is computed for multiple comparisons of mean rank sums test between all cell populations. If the p-values between the candidate cell population and the other populations are all below the specified threshold (0.01), then the gene E will be considered as label specific marker gene of the candidate cell population.
H Chen, L Albergante, JY Hsu, CA Lareau, GL Bosco, J Guan, S Zhou, AN Gorban, DE Bauer, MJ Aryee, DM Langenau, A Zinovyev, JD Buenrostro, GC Yuan, L Pinello Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nature Communications, volume 10, Article number: 1903 (2019)
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20-31 (2016).
Pinello Lab STREAM Github Repository
Example data for the STREAM workflow can be downloaded from dropbox: Stream Example Data
Ref: Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20-31 (2016).
Example data for this specific step can be found at stream_epg_result.pkl
GenePattern 3.9.11 or later (dockerized).
Inputs and Outputs | |
| Name | Description |
|---|---|
| data file* | A STREAM pkl file containing an annotated AnnData matrix of gene expression data/td> |
| output filename* | The output filename prefix. |
Marker Gene DetectionParameters used if variable genes are to be selected as the feature. | |
| Name | Description |
| root | The starting node. |
| preference | The preference of nodes. The branch with speficied nodes are preferred and put on the top part of subway plot. The higher ranks the node have, the closer to the top the branch with that node is. e.g. S3,S4. |
| percentile expr* | Between 0 and 100. Between 0 and 100. Specify the percentile of gene expression greater than 0 to filter out some extreme gene expressions. |
| use precomputed* | If True, the previously computed scaled gene expression will be used. |
| cutoff zscore* | The z-score cutoff used for mean values of all cell populations. |
| cutoff pvalue | The p value cutoff used for Kruskal-Wallis H-test and post-hoc pairwise Conover's test. |
* - required


Here's a timeline of M.S.Dhoni's life as an Indian captain:
November 6, 2008: Takes charge as full-time skipper after Kumble's retirement. India beats Australia by 172 runs in Nagpur to win the series 2-0.
December 23, 2008: In his first full series as captain, leads India to a 1-0 win over England.
April 7, 2009: Becomes only the second Indian captain in 41 years to win a Test series in New Zealand.
December 6, 2009: Following a 2-0 series win over Sri Lanka, India becomes No.1 in the Test rankings for the first time.
February 18, 2010: India retains its No.1 ranking with an innings win over South Africa at Eden Gardens. Dhoni scores 132 not out off 187 balls in India's 643 for six in series-levelling win.
January 6, 2011: India draws its first Test series on South African soil.
August 22, 2011: India loses No.1 ranking after suffering a 4-0 whitewash against England.
January 2012: Suffers another 4-0 whitewash, this time in Australia.
December 17, 2013: A rare failure at home as England wins the four Test series 2-1. India's first home Test series loss in 28 years.
February 24, 2013: Hits a rollicking maiden double-century (224) against Australia in Chennai. It is the highest score by an wicketkeeper-captain and the best by an Indian keeper. India wins the match.
March 5, 2013: Dhoni becomes India's most successful captain with 22 wins. An innings and 135 run win over Australia in Hyderabad takes hm past Sourav Ganguly's record of 21.
March 24, 2013: Oversees India's first Test series whitewash over Australia.
December 30, 2013: A third consecutive overseas Test series loss as India goes down 1-0 in South Africa.
February 2014: India loses a Test and the series to NZ after 12- years — a fourth straight overseas defeat.
July 21, 2014: India wins at Lord's for the first time in 28 years.
August 17, 2014: A three-day defeat at The Oval sees India lose to England 3-1, a fifth straight away series loss under Dhoni.
December 30, 2014. Shortly after India loses its sixth consecutive overseas Test series, Dhoni announces his retirement from Test cricket.
I focus on cricket because it's something I am good at. After retiring, I want to serve in the army. It has always been about serving the nation.
If you want to read more about him, please check his Wikipedia entry.


VIMDIFF(1) VIMDIFF(1) NAME vimdiff - edit two, three or four versions of a file with Vim and show differences SYNOPSIS vimdiff [options] file1 file2 [file3 [file4]] gvimdiff DESCRIPTION Vimdiff starts Vim on two (or three or four) files. Each file gets its own window. The differences between the files are highlighted. This is a nice way to inspect changes and to move changes from one version to another version of the same file. See vim(1) for details about Vim itself. When started as gvimdiff the GUI will be started, if available. In each window the 'diff' option will be set, which causes the differ‐ ences to be highlighted. The 'wrap' and 'scrollbind' options are set to make the text look good. The 'foldmethod' option is set to "diff", which puts ranges of lines without changes in a fold. 'foldcolumn' is set to two to make it easy to spot the folds and open or close them. OPTIONS Vertical splits are used to align the lines, as if the "-O" argument was used. To use horizontal splits instead, use the "-o" argument. For all other arguments see vim(1). SEE ALSO vim(1) AUTHOR Most of Vim was made by Bram Moolenaar, with a lot of help from others. See ":help credits" in Vim. 2001 March 30 VIMDIFF(1)


Thinking is a large part of the unseen work designers do to develop engaging, appropriate and award-winning work. During a redesign of my website I was thinking about what I wanted to say about my process. There was a moment of realization where two ideas seemed to connect to open a more broad view of that process. It is rare that these occasions happen so I figured writing down my conclusions would be helpful, not only to confirm my thoughts, but also to make more connections.
Many young designers believe that good ideas come by inspiration or just magically appear; and that only a select few strike the perfect idea. This phenomenon seems evasive until further investigation into the process of thinking. It is true that some are able to capture good ideas more often, but those people have learned through experience that good ideas come from good thinking.
Through my inquiry into thinking I have identified a two-part pattern in thinking that has allowed solid ideas to be developed more successfully. The first is by actively participating in the thinking process. Actions we initiate that lead to idea development, such as writing, sketching, talking, reading, actively engage our brains in making connections. These types of activities are the foundation for active thinking. I have found that when I doodle or write, I am more able to make my ideas concrete and understandable. This process often leads to surprising results, sometimes totally unrelated to the immediate problem I am trying to solve.
Another essential part of active thinking is that during the process, a hard copy reference point is created for future projects or improvements to previous ideas. Most designers use a sketchbook as this reference. I have laid out my sketchbook to optimize the active thinking process for me. It contains separate areas for writing and drawing that allow me to keep my thoughts more organized than a muddled mass of words and images.
The second part is more passive and elusive. It may even seem to be “unthinking.” Passive thinking often comes when we least expect it. Some may call it inspiration, others luck or intuition. Whatever its label, the results are the same—an instant where our brains have made a connection between two seemingly disconnected ideas. Most often there is no deliberate action taking place to think in this way. These moments sometimes materialize in daydreams, at the gym, or while traveling. There is more going on during the moments when we seem to not think at all than when we are actively thinking. Meditation, sleeping and daydreaming provide respite from the strain of active thinking and balance the process. Complete distraction may also be a form of passive thinking. If we focus on something totally unrelated to the problem at hand we often re-approach it with new insights.
Once the right balance between active and passive thinking has been struck the ideas start pouring out. Then its just a matter of finding the most appropriate idea, which is another skill altogether.


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Interface IServicingClient
Monitors and completes service requests.
Inherited Members
BaseClients.Architecture.ICallbackClient.IsConnected
BaseClients.Architecture.ICallbackClient.Key
BaseClients.Architecture.ICallbackClient.Heartbeat
BaseClients.Architecture.ICallbackClient.Connected
BaseClients.Architecture.ICallbackClient.Disconnected
BaseClients.Architecture.IClient.EndpointAddress
BaseClients.Architecture.IClient.LastCaughtException
BaseClients.Architecture.IClient.Logger
System.IDisposable.Dispose()
Namespace: SchedulingClients.Core
Assembly: SchedulingClients.Core.dll
Syntax
public interface IServicingClient : ICallbackClient, IClient, IDisposable
Methods
| Improve this Doc View SourceGetOutstandingServiceRequests()
Gets all outstanding service requests.
Declaration
IServiceCallResult<ServiceStateDto[]> GetOutstandingServiceRequests()
Returns
| Type | Description |
|---|---|
| GAAPICommon.Architecture.IServiceCallResult<ServiceStateDto[]> | Array of service state dtos. |
SetServiceComplete(Int32)
Marks a service task as complete.
Declaration
IServiceCallResult SetServiceComplete(int taskId)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | taskId | target task id |
Returns
| Type | Description |
|---|---|
| GAAPICommon.Architecture.IServiceCallResult | Successful service call result on success |
Events
| Improve this Doc View SourceServiceRequest
Fired whenever a new service request is received.
Declaration
event Action<ServiceStateDto> ServiceRequest
Event Type
| Type | Description |
|---|---|
| System.Action<ServiceStateDto> |


import numpy as np
import pandas as pd
from inspect import currentframe, getargvalues
from pandas.api.types import is_numeric_dtype
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.impute import SimpleImputer
from statsmodels.stats.outliers_influence import variance_inflation_factor
[docs]class VIFSelector(BaseEstimator, TransformerMixin):
"""Makes feature selection based on Variance Inflation Factor.
Calculates Variance Inflation Factor for a given dataset, in each iteration
discarding the variable with the highest VIF value and repeats this
process until it is not below the declared threshold.
Based on:
https://www.kaggle.com/ffisegydd/sklearn-multicollinearity-class
Parameters
----------
thresh: float (default: 5.0)
Threshold value after which further rejection of variables is
discontinued.
impute: boolean (default: True)
Declares whether missing values imputation should be performed.
impute_strat: string {'mean', 'median', 'most_frequent'} (default: 'mean')
Declares imputation strategy for the scikit-learn SimpleImputer
transformation.
verbose: int, default = 0
Controls verbosity of output. If 0 there is no output, if 1 displays
which features were removed.
Attributes
---------
imputer_: estimator
The estimator by means of which missing values imputation is performed.
viffed_cols_: list
List of features from a given dataset that exceeded thresh.
kept_cols_: list
List of features that left after the vif procedure.
"""
def __init__(self, thresh=5.0, impute=True,
impute_strat='mean', verbose=0):
icf = currentframe()
args, _, _, values = getargvalues(icf)
values.pop('self')
for param, value in values.items():
setattr(self, param, value)
if impute:
self.imputer_ = SimpleImputer(strategy=impute_strat)
[docs] def fit(self, X, y=None):
"""Fits columns with a VIF value exceeding the threshold.
If specified, fits the imputer on X.
Parameters
----------
X: DataFrame, shape (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features
is the number of features.l
Returns
-------
self: object
Returns the instance itself.
"""
if hasattr(self, 'imputer_'):
self.imputer_.fit(X)
self.viffed_cols_, self.kept_cols_ = self._viffing(
X, self.thresh, self.verbose
)
return self
@staticmethod
def _viffing(X, thresh, verbose):
"""In every iteration removes variable with the highest VIF value."""
assert isinstance(X, pd.DataFrame), \
'Input must be an instance of pandas.DataFrame()'
assert ~(X.isnull().values).any(), (
'DataFrame cannot contain any missing values, consider changing '
'impute parameter to True.'
)
assert all(is_numeric_dtype(X[col]) for col in X.columns), \
'Only numeric dtypes are acceptable.'
X_new = X.copy()
viffed_cols = []
keep_digging = True
while keep_digging:
keep_digging = False
if len(X_new.columns) == 1:
print("Last variable survived, I'm stopping it right now!")
break
vifs = [
variance_inflation_factor(
X_new.values,
X_new.columns.get_loc(var)
) for var in X_new.columns
]
max_vif = max(vifs)
if max_vif > thresh:
max_loc = vifs.index(max_vif)
col_out = X_new.columns[max_loc]
if verbose:
print(
'{0} with vif={1:.2f} exceeds the threshold.'
.format(col_out, max_vif)
)
X_new.drop([col_out], axis=1, inplace=True)
viffed_cols.append(col_out)
keep_digging = True
kept_cols = X_new.columns.tolist()
return viffed_cols, kept_cols
[docs]class CorrelationReducer(BaseEstimator, TransformerMixin):
"""Removes correlated columns exceeding the thresh value.
Parameters
----------
method: string, optional (default='pearson')
Compute pairwise correlation of columns, excluding NA/null values
(based on pandas.DataFrame.corr).
- `pearson`: Standard correlation coefficient.
- `kendall`: Kendall Tau correlation coefficient.
- `spearman`: Spearman rank correlation.
thresh: float (default=.8)
Threshold value after which further rejection of variables is
discontinued.
Attributes
----------
correlated_cols_: list
List of correlated features from a given dataset that exceeded thresh.
"""
def __init__(self, thresh=.8, method='pearson'):
icf = currentframe()
args, _, _, values = getargvalues(icf)
values.pop('self')
for param, value in values.items():
setattr(self, param, value)
[docs] def fit(self, X, y=None):
"""Fits columns with a correlation coefficients exceeding the threshold.
Parameters
----------
X: DataFrame, shape (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features
is the number of features.
Returns
-------
self: object
Returns the instance itself.
"""
assert isinstance(X, pd.DataFrame), \
'Input must be an instance of pandas.DataFrame()'
self.correlated_cols_ = self._reduce_corr(X, self.thresh, self.method)
return self
@staticmethod
def _reduce_corr(X, thresh, method):
"""Returns correlated columns exceeding the thresh value.
"""
df = X.corr()
# Create matrix of ones of the same size as the dataframe
arr_one = np.ones(shape=df.shape, dtype=bool)
# Set the value above the main diagonal to zero creating L-matrix
L_arr_one = np.tril(arr_one)
df.mask(L_arr_one, other=0., inplace=True)
corr_cols = (df.abs() >= thresh).any()
cols_out = corr_cols[corr_cols == True].index.tolist()
return cols_out


Software Engineer with a demonstrated history of working in the computer software industry. Skilled in a variety of technologies from front end clients, to backend microservices, data processing methodologies and automated test frameworks. Experience managing and working with distributed teams, ensuring quality for enterprise level products. Strong engineering professional with a Bachelor of Science (B.S.) focused in Computer Science from University of Washington Bothell.
Frontend Web Development
WebRTC Development and Triage
Distributed Teams
Agile Project Management
Automated Testing Frameworks
Continuous Integration
Agile Methodologies
Frontend Web Development
Backend Microservice Development
Automated Testing
Big Data
Automated Data Processing
Data Analysis
Extract Transform Load
Programmatic Decompression
Internal Tools Development
Maintenance of: websites using Kentico CMS, student details using StarRez, internet services for residents
Implemented a scalable, dynamic, and inexpensive solution for backend inventory management and analytics integrating multiple data sources using Microsoft Excel and utilization of VBA macros
Research and comparative analytics on external organizations relative to pricing, amenities, programs
Management of supplies and improvements for Conference Housing, research and ordering
Analyzed over 600 scholarly publications and interpreted qualitative and quantitative data
Compiled research into cohesive papers and reviewed 5+ papers preparing for publication
3.84 GPA
And a few other things ... :)
Outside of being a software engineer, I enjoy spending quality time with my wonderful daughter, and being outdoors. I appreciate activities such as hiking, backpacking, snowboarding, sailing, snowmobiling, fishing, squidding, snowshoeing, boating, wakesurfing, swimming, and lawn games.
When I'm located under a roof, I enjoy organizing and researching outdoor gear, locations, and activities, love cooking, writing code, playing games/reading/writing/math/science with my daughter, going to the gym, and unfortunately, spending some time in front of the tube.


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EXCLUSIVE: Godfrey: Stevie Wonder Threatened to Stab Me if I Made Jokes About Him
by Jessica ToddGodfrey stopped in for another VladTV interview, and he spoke about meeting people on the streets that have binge-watched his interviews. He also spoke about meeting Stevie Wonder while hosting a Mix Magazine awards show, where he spoke to the legendary musician. Godfrey shared a story about Stevie saying that he would stab Godfrey if he made a joke about him on stage. Godfrey also touched on hearing that Stevie was having some health issues, which led to a conversation about celebrities not going to the hospital, like Bob Marley. To hear more of what he had to say, hit the above clip.


Comments
-
Greg Hurrell
I can't reproduce this bug.
The Floater respects the position of the Dock (ie. when it calculates the "visible screen area" it excludes the Dock). If you move the Dock, the visible screen area changes, and the Floater position changes. If you move it back, the area changes back and the Floater moves back. At no point does the Floater move of screen.
On which edge of the screen is your Dock? Mine is on the bottom and no amount of hiding or unhiding the Dock can make the Floater move off screen.
-
William Hall
It's definitely reproducible on my machine. If you can suggest a software-based way to record a movie of the display, I'll do so. For now I've tried ordered screenshots.
My dock is bottom-oriented on the left-hand of two displays. The floater is bottom-right of the left- hand display. I took screenshots with and without dock hiding. Page 1 of each is the left-hand display, and page 2 is the right-hand display. They'll also give you an idea of the size of my dock.
Without hiding: http://analogkid.net/Picture1.pdf
And the only change to the next picture is to toggle dock-hiding via right-click interface on the dock, then re-display the floater: http://analogkid.net/Picture2.pdf
I think the problem stems from my setting the floater position with dock-hiding off such that the bottom of the floater is below the top edge of the dock. Starting from scratch, here is me setting the floater position with dock hiding off. The floater is completely on-screen. http://analogkid.net/Picture3.pdf
I then display the floater, and it's still fully on-screen. http://analogkid.net/Picture4.pdf
But if I turn on dock hiding and show the floater again it is off-screen. http://analogkid.net/Picture5.pdf
None of this is inconsistent with these statements of yours - this all matches the behavior that I see:
The Floater respects the position of the Dock (ie. when it calculates the "visible screen area" it excludes the Dock). If you move the Dock, the visible screen area changes, and the Floater position changes. If you move it back, the area changes back and the Floater moves back.
Except this one of course, which is contrary to the description of the bug.
At no point does the Floater move off screen.
Can you reproduce by setting the floater with dock-hiding on as I have in screenshot #3?
I should expect that in addition to moving along with the dock as it grows/shrinks in size or moves up/ down due to hiding, that the floater would never move such that it's bottom edge was off-screen.
-
William Hall
This happens on my powerbook at home, single-display, same sort of position for the floater (lower edge below the top of Dock, bottom right anchored, Dock on bottom). That rules out dual-display at least.
-
Greg Hurrell
Ok, thanks for posting the screenshots. Now I know what's happening.
This is consistent with what I described above. As I said, when it calculates the "visible screen area" it excludes the Dock. To be more precise, it uses the visibleFrame method in Cocoa's NSScreen class. According to this method, the place where you have the Floater is actually outside of the visible screen area (because the bottom of the floater is below the top edge of the Dock, which is what defines the border of the visible screen area for Cocoa; the "visible screen area" is a rectangle, and the blank spaces on either side of the Dock are not part of it). So when you later hide the dock, the visibleFrame changes (gets a bit bigger) and the Floater moves to maintain its old position, which is several pixels below the visible screen area.
There is no way to fix this "bug" and have Synergy continue to respect the position of the Dock. There are no methods in Cocoa for determining how tall the Dock is or how wide (nor in Carbon or any other API as far as I know). Basically the APIs assume that if you ask for the visible frame rectangle then you will not put things in the space on either size of the Dock. It's a "no go" zone.
The only way to resolve the issue would be to ignore the Dock entirely when calculating the screen size (using the frame method instead of the visibleFrame method). This is a shame because many people position the Floater above the Dock and if I make the change then they will consider the new behaviour to be a bug (people will see this who have a Dock that occupies the entire width of their screen; as they add and remove items it will grow and shrink, and the Synergy Floater should adjust itself along the way).
So I am going to leave this bug report open for a while and will wait for comments before making a decision about how to handle it.
-
Greg Hurrell
See this forum posting:
http://wincent.com/a/support/forums/showflat.php?Number=806
Another user would like Dock-independent positioning of the Floater. Sounds like there is enough interest in implementing this to make it worthwhile (ie. as an option, not as a default behaviour).
-
Greg Hurrell
Changing the summary and flagging this as an enhancement request.
-
Greg Hurrell
Changing assignment to reflect my new email address.
http://wincent.com/a/news/archives/2006/05/change_of_email.php
Add a comment
Comments are now closed for this issue.


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This is version 2 of the genetic algorithm. The obvious change is that it provides control over the terminating condition, but it also addresses some issues with inbreeding. Inbreeding happens when a local minima is reached, and most gene changes result in a poorer performance. This results in a convergence of the individuals, as those individuals that share the most genes with the optimum individual survive. As those similar individuals end up breeding, all genes but the ones that are optimized for that local minimum are selected out, leaving a population with a large number of clones. The mutations and exogamous individuals will eventually break out of the local minimum, but it may take many generations.
This version examines each breeding pair, and if the pair are identical, the are mutated rather than bred. Clones will still appear, but the inbreeding check will prevent them from multiplying and taking over the surviving population. The program now also tracks the history of each individual, and displays it at the end, like this:
Ancestry: mutated bred bred bred bred mutated mutated bred bred bred bred mutated mutated bred mutated mutated bred mutated initialized
That shows the history of that individual; in that case, the path was of short length from a randomly initialized individual to the optimum, through breeding and a few mutations. The origin will be either "initialized", indicating the individual traces its ancestry back to one of the originals, or "exogamy", meaning it was one of the random individuals added at a later generation. There is no family tree, since individuals don't reproduce, but rather clone themselves and then engage in gene swapping, "bred" or mutation, "mutation".
Allowable squared error (in pixels): Step delay (in ms):


| Function name | Function info |
|---|---|
ceil(1) : ceil(x), where x is a float value, returns the smallest integer no smaller than x. ceil(2) : ceil(x,y), where x is a float value and y is a positive integer, returns the smallest float value with y digits after decimal point no smaller than x. |
|
fact(1) : Function fact(x) calculates factorial of a non-negative integer x. |
|
floor(1) : floor(x), where x is a float value, returns the largest integer no larger than x. floor(2) : floor(x,y), where x is a float value and y is a positive integer, returns the largest float value with y digits after decimal point no larger than x. |
|
conv_bin_to_dec(1) : conv_bin_to_dec(x) converts a non-negative binary value or string x (can be float or integer) to a decimal value. |
|
conv_bin_to_hex(1) : conv_bin_to_hex(x) converts a non-negative binary value or string x (can be float or integer) to a hexadecimal string. |
|
conv_bin_to_oct(1) : conv_bin_to_oct(x) converts a non-negative binary value or string x (can be float or integer) to an octal string. |
|
conv_dec_to_bin(1) : conv_dec_to_bin(x) converts a non-negative decimal value or string x (can be float or integer) to a binary string. |
|
conv_dec_to_hex(1) : conv_dec_to_hex(x) converts a non-negative decimal value or string x (can be float or integer) to a hexadecimal string. |
|
conv_dec_to_oct(1) : conv_dec_to_oct(x) converts a non-negative decimal value or string x (can be float or integer) to an octal string. |
|
conv_hex_to_bin(1) : conv_hex_to_bin(x) converts a non-negative hexadecimal value or string x (can be float or integer) to a binary string. |
|
conv_hex_to_dec(1) : conv_hex_to_dec(x) converts a non-negative hexadecimal value or string x (can be float or integer) to a decimal value. |
|
conv_hex_to_oct(1) : conv_hex_to_oct(x) converts a non-negative hexadecimal value or string x (can be float or integer) to an octal string. |
|
conv_oct_to_bin(1) : conv_oct_to_bin(x) converts a non-negative octal value or string x (can be float or integer) to a binary string. |
|
conv_oct_to_dec(1) : conv_oct_to_dec(x) converts a non-negative octal value or string x (can be float or integer) to a decimal value. |
|
conv_oct_to_hex(1) : conv_oct_to_hex(x) converts a non-negative octal value or string x (can be float or integer) to a hexadecimal string. |
|
is_prime(1) : Function Is_Prime(x) is used to determine if a positive integer x(>=2) is a prime number or not. If x is not a positive integer or it is less than 2, return false. |
|
mod(2) : mod(x,y) returns the remainder of x/y (y must be positive). If x and y are not integers, they will be converted to integer numbers first. |
|
round(1) : round(x) returns rounded integer value of x. round(2) : round(x,y) returns rounded value of x with y digits after decimal point. |


Symbol Index for "Image Valued Characteristic"
Symbol Index for diagram
"Image Valued Characteristic"
Category Valued Characteristic (Class)
Image Valued Characteristic (Class)
Measured Characteristic (Class)
SCH/Value Type (Generalization Node)
Subject Characteristic (Class)
Text Valued Characteristic (Class)
Timepoint Valued Characteristic (Class)


For Love of the Game Reviewed by Eugene Novikov http://www.ultimate-movie.com Member: Online Film Critics Society
*** out of four
"Haven't you ever loved anything that much?"
Starring Kevin Costner, Kelly Preston, John C. Reilly. Rated PG-13.
As Waterworld and The Postman prove, Kevin Costner fares better, both cinematically and financially, when he surrenders the director's chair to one of his betters and constrains himself to merely being the star. In For Love of the Game, Costner's triumphant return to baseball (see Field of Dreams and Bill Durham), the man behind the camera is Sam Raimi (The Evil Dead, A Simple Plan), an unpretentious yet sublimely confident auteur with an amazing range (he went from cheesy horror to crime drama to baseball romance). Raimi's latest is also the best we've seen from Costner since Tin Cup; an engaging, interestingly structured drama nearly botched by its denoument. Those who seek to have an unabashedly positive memory of the film would do well to leave right after its baseball game centerpiece, the place where it should have been cut off.
For Love of the Game has a baseball backdrop but it is not a "baseball movie". It's about Billy Chapel (Costner), a great pitcher in the twilight of his career, who has just been told that he is being traded from the Detroit Tigers; the team he has played with for some 19 years. At the same time, he finds out that Jane (Kelly Preston), his long-time girlfriend decided to leave him. As he goes into his last game of the season, his last game with the Tigers and possibly his last game, he remembers and examines important moments in his roller coaster relationship, discovering more about it than he did during its course.
They met when he saw Jane's car stalled on the side of a highway, with Jane kicking the living headlights out of the poor vehicle. Being a nice guy, Billy stops to help, and this being Hollywood, he invites her to go to a baseball game with him. One thing leads to another and they wind up together in Billy's hotel room. Now that they've slept together, he thinks, they're in a relationship, but not so fast: Jane shuns the idea, wanting to make sure she does not become a "groupie".
After being chased enough times by Billy, she gives it up and the three (Jane has a daughter) move in together. They are doing wonderfully until he badly hurts his arm in a woodshop accident. He is determined to make a comeback and return to the mound; his trainer and his girlfriend are doubtful and less than supportive. Without thinking the matter through, Billy explodes and makes Jane leave; an action he comes to regret and attempts to reconcile.
The love story is told almost exclusively through flashback, the film cutting back and forth between it and the all-important baseball game (in which, by the way, Chapel tries for a perfect game). It's not the most original style of storytelling, but it works because Raimi effectively and subtly injects the flashbacks with a common theme. Indeed, up to the last ten minutes, I had a sneaking suspicion that this might be that rare Hollywood love story that's actually about something. It built up enough momentum that I was ready for a conclusion to drive its point home. Instead, what has been a compelling tale of a man learning to depend on himself turns into a banal, typical, hollow romance.
This cruel bait-and-switch can only detract from the film to a certain point. Despite its eventual thematic inadequacies it remains a perfectly entertaining Tinseltown production. Under the sure direction of Sam Raimi, it's a lot of fun to watch, even if it's not much else. The suspense of the baseball game is genuine (though the trailers thoughtlessly gave away the result) and the romance feels real (most of the time). There is a fight scene (relationship quarrel, not martial arts) that is so well-written it's devastating.
Kevin Costner does his job in a relatively straightforward role; though he has his moments and though I can't point out anything specifically wrong with what he does, he still tends to fall short of outstanding. His character is engaging but not really because of Costner's performance. Kelly Preston, on the other hand, is wonderful. We can understand why a man would be so interested in her, because we are equally enchanted. Jane's daughter, played well by Oscar winner Anna Paquin, is an unusual character because she is not strapped with the cliche of hating her mom's new boyfriend; a refreshing occurence as almost all movie kids in a single-parent home are required to despise their parent's new love interest.
I enjoyed this movie so much that I watched with my hands on my head in desperation as it grossly undermined itself in the last act. It kept itself from being anything beyond a solid and entertaining but typically empty mainstream drama. It's a fun movie -- I recommend it for that -- but little more. ©1999 Eugene Novikov
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It’s true: when abused, no drug is a safe drug. Ativan easily falls under this category. Because Ativan is highly addictive, most in the medical profession suggest use of the drug be supervised by a doctor or clinical expert.
Secondly, Ativan should also be prescribed for short periods of use because of the strong risk of physical and psychological dependence that the drug causes.
Of course, the most severe side effects associated with Ativan use occur during exposure to high doses of the drug. These side effects can include:
Everyone’s addiction is different. This is because each person processes drugs differently. Much depends on their overall health, age, weight, as well as exposure to other drugs, etc. All this information, including how high a dosage and how long a person is a user of Ativan can determine whether or not they require in or outpatient rehabilitation, and how intense that rehab should be.
It isn’t the easiest of calls, but a qualified rehabilitation center with a certified staff can offer comprehensive suggestions as to how you or your loved one should approach their recovery from Ativan.
If you fear you have an issue with Ativan, check yourself over for these withdrawal symptoms that can come up if you temporarily stop using the drug.
Don’t let Ativan addiction hang around. The last thing a dependency like this needs is to linger, particularly with the adverse effects the drug has.
Instead of just dealing with the drug, or attempting to detox and rehab on your own (which is a very dangerous proposition as it is), seek out the best rehabilitation facility you can find within reason of your budget as well as the quality of care you expect toward your recovery.
In the end, addressing you or your loved one’s addiction to Ativan, is an issue that requires attention right now.


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Angelina Jolie hints she's recovering after 'painful' Brad Pitt divorce as she talks heartache
Angelina revealed she's on the path to recovery after seeing the ones she loves "in pain" following the divorce
by Lily Waddell, https://www.mirror.co.uk/authors/lily-waddell/Angelina Jolie has suggested she is recovering after her "hurtful" divorce from Brad Pitt.
They grabbed the headlines with their romance for 12 wonderful years, two of which the exes were married from 2014 until their split in 2016.
The actress, 44, revealed she's on the path to recovery after seeing the ones she loves "in pain".
Angelina admitted she "wrestled" with her heartache over the divorce as she admitted she had got "hurt".
She told December's Harper's Bazaar magazine: "My body has been through a lot over the past decade, particularly the past four years, and I have both the visible and invisible scars to show for it."
Of heartache, she added: "The invisible ones are harder to wrestle with. Life takes many turns. Sometimes you get hurt, you see those you love in pain, and you can’t be as free and open as your spirit desires. It’s not new or old, but I do feel the blood returning to my body."
Elsewhere in the interview, the Maleficent star claimed the Fury actor has stopped her from moving to her dream home abroad.
The duo have three biological children (Shiloh, 13, Vivienne, 11, and Knox, 11) as well as three adopted children (Maddox, 18, Pax, 15, and Zahara, 14).
As they continue to co-parent their children, Angelina talked of the restrictions Brad had put on her being able to move as the couple continue to co-parent their children together.
The actress vowed to move as soon as her youngest child turns 18 but until then she has to remain where she said Brad "chooses to live".
She told the glossy magazine: "I would love to live abroad and will do so as soon as my children are 18. Right now I’m having to base where their father chooses to live."
Their eldest son Maddox is at university abroad in South Korea.
Angelina and Brad's divorce finance battle is ongoing but the couple went public with their split in 2016.


TO_YMINTERVAL
converts a text expression to an INTERVAL YEAR TO MONTH
data type. The function accepts argument in one of the two formats:
SQL interval format compatible with the SQL standard (ISO/IEC 9075:2003)
ISO duration format compatible with the ISO 8601:2004 standard
INTERVAL YEAR TO MONTH
TO_YMINTERVAL ( ' { sql_format | ym_iso_format } ' ) sql_format::= [+|-] years - months ym_iso_format::= [-] P [ years Y ] [months M ] [days D ] [ T [hours H ] [minutes M ] [seconds [. frac_secs] S ] ]
In SQL format:
years
is an integer between 0 and 999999999
months
is an integer between 0 and 11.
Additional blanks are allowed between format elements.
In ISO format:
years
and months
are integers between 0 and 999999999.
days
, hours
, minutes
, seconds
, and frac_secs
are nonnegative integers and are ignored.
No blanks are allowed in the value.
TO_YMINTERVAL('1-6')
and TO_YMINTERVAL('P1Y6M')
return the value +01-06
for 1 year and 6 months.
SYSDATE + TO_YMINTERVAL('1-6')
adds one year and six months to the current date. When SYSDATE
is 15-APR-08
, the value is 15-OCT-09
.
SYSDATE + TO_YMINTERVAL('P1Y6M')
adds one year and six months to the current date using ISO format. When SYSDATE
is 15-APR-08
, the value is 15-OCT-09
.
SYSDATE + TO_YMINTERVAL('-1-2')
subtracts one year and two months from the current date. When SYSDATE
is 15-APR-08
, the value is 15-FEB-07
.


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Total paid by city to police:


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Description:


Top N Scores. Show only N taxons with best scores
Show Others. The taxons that are not in Top N Score will be resumed by one generic group.
Sample Type. This define the scores for each taxon. If "binary" was choosed, then the chart will show the scores relative discriminative capacity between "CSTR" and "UASB" for each taxon. If "grouped" was choosed, then the discriminative criteria will consider the groups: "UASB1", "UASB2", "CSTR1", "CSTR2" and "CSTR3"
The dataset used to evaluate the scores of each taxon using Random Forest. The "filtering" of some taxons can cause considerable changes in the score.
Taxa level scores (Y axis). This will define the scores values, related to the choosed leve.
Taxa level grouped (X axis). Each score will be grouped by this taxonomic level (stack bars). Note that it value only make sense if you choose a level with a higher value than "Taxa level scores".


Finds all the ancestors of an element up until the element matched by the specified selector.
Node.parentNode
and a while
loop to move up the ancestor tree of the element.Array.prototype.unshift()
to add each new ancestor to the start of the array.Element.matches()
to check if the current element matches the specified selector
.


Looking forward to working with the Sparkinity team as they really understand the pressures of buying, I am looking for new products everyday and can't wait to try out their market test feature
Owner of Space London
As a small store we are constantly on our toes to delight our customers and keep them coming back for more. Its a very competitive market and finding unique products that work with our brand values & price architecture is hard. Sparkinity seems like a promising & valuable concep
Owner of Workshop
Having launched my home decor brand last year, I have been struggling with trade fairs and connecting with buyers as its so expensive and time consuming. Sparkinity is an amazing concept. I have been waiting for this for a long time
Maker of ceramics


Oral statement to Parliament
Minister of State for Schools David Laws makes an announcement on school accountability.
Mr Speaker,
I would like to make a statement on the future of secondary school accountability, following our recent consultation . But may I first welcome to his post the new Shadow Secretary of State, and can I express our best wishes to his predecessor the Honourable Member for Liverpool West Derby.
Until now secondary schools have been judged by the proportion of pupils awarded 5 GCSEs at grade C or better, including English and maths.
Schools currently improve their league table position if pupils move over the C/D borderline. This gives schools a huge incentive to focus excessively on the small number of pupils around the 5 Cs borderline. This is unfair to pupils with the potential to move from E grades to D grades, or from B grades to A grades.
It is also, paradoxically, unfair to those on the C/D borderline because it leads schools to teach to the test. Ofqual, the chairman of the Education Select Committee and others are all clear that this is the case. Indeed, all 5 of the maths organisations said that the current approach harmed the teaching of mathematics. The Association of Teachers of Mathematics, for example, said that “Teaching to the test results in superficial skills development which means that students are ill prepared for adult life.”
Furthermore, as Chris Paterson at CentreForum has shown, the current accountability framework discourages schools from focusing on the lowest attaining pupils.
In a recently published book ‘The Tail’, the authors argued that the last 15 years have seen rises in average attainment, but not in the attainment of those at the bottom. International surveys such as TIMMS confirm this position. We need secondary schools to give more attention to pupils who are falling behind.
The current measure also permits many schools, particularly in affluent areas, to ‘coast’. These schools find it easy to hit targets based on 5 C grades. The school may look successful, but Cs are not a success if pupils are capable of more. The accountability system must set challenging but fair expectations for every school, whatever its intake.
The 5 A* to C measure also encourages schools to offer a narrow curriculum. Mastery of just 5 subjects is not enough for most pupils at age 16.
Furthermore, the use of equivalent qualifications means that some students have not been offered a rigorous academic curriculum that would have served them well. Until this year, a school could offer English, maths and only 1 BTEC and still have the pupil count as having achieved 5 Cs or better.
We believe that we can do better.
We will require all schools to publish core information on their website, in a standard format. From now on, there will be 4 key measures which must be published:
We will also look at including a destination measure to show the percentage of pupils who move on to further study or employment - including further training.
We are proposing an important change to the way we measure underperformance and to our floor targets. Rather than the 5 A* to C GCSE threshold measure, we will use the new progress measure. This is much fairer because it takes account of a school’s intake.
A pupil’s key stage 2 results, achieved at the end of primary school, will be used to set a reasonable expectation of what they should achieve at GCSE. Schools will get credit where pupils outperform these expectations.
This approach will ensure that all pupils matter, and will matter equally.
This approach is fairer for schools as well as pupils.
Coasting schools will no longer be let off the hook. Equally, headteachers will no longer feel penalised when they have actually performed well with a challenging intake. We must not deter the best headteachers and teachers from working in challenging schools.
Pupils’ progress and attainment will be assessed in 8 subjects: English and maths, 3 further EBacc subjects, and 3 other high-value qualifications. This final group can include further traditional academic subjects, subjects such as art, music and drama, and vocational subjects, such as engineering and business. English and maths will be double weighted to reflect the importance of these subjects.
This will encourage schools to offer all pupils a broad curriculum with a strong academic core.
We will define the new floor standard as progress half a grade lower than reasonable expectations. So, if pupils at a school are expected to average a B in their 8 subjects, the school will be below the floor if they average less than 4 Bs and 4 Cs.
At present, there are 195 schools below the floor standard. Using existing figures, we estimate that around twice as many schools would be below this new floor standard. However, as schools will adjust their curriculum to the new framework the actual number is likely to be significantly lower than this.
We also want to recognise schools in which pupils make exceptional progress. So, a school in which pupils average a full grade above reasonable expectations will not be inspected by Ofsted in the following year. This is the first time that the accountability regime has offered schools a carrot, as well as a stick.
Schools have planned their current curriculum for years 10 and 11 on the basis of the existing accountability system. For that reason the new system will begin in 2016, that is, for students currently in year 9.
We will, however, allow schools to opt in to the new system from 2015.
The government response to the consultation also describes how we will publish the information we hold about secondary schools through a new data portal. This builds on our existing performance tables, and will allow all interested groups - governors, parents, academics and civil society more widely - to analyse many aspects of school performance.
Our full response to the consultation is available on the Department for Education’s website, and a copy will be placed in the Library of the House.
Mr Speaker, we are removing perverse incentives for schools to act in a way which is not in the best interests of pupils. More pupils will get the teaching they require and will obtain the valuable qualifications they need.
These proposals will have a major and positive effect on our education system. We hope they will secure support from across the political spectrum.
Published: 14 October 2013


District Championship Results
Here's a big Thank You to all the racers, volunteers, sponsors, and families
who made the District Championships possible. If you were there, you have
to agree that the weather and racecourses were perfect for good racing.
The old Cow Country Classic road course dished out all of the challenges for
which it is renowned, except for the occasional cattle drives that we've seen
in past years. The racing was fast and furious in the Master/Cat 4,5 pack.
That must have been true for each of the other fields since the largest group
to finish together in any of the fields was no more than 3 riders.
Congratulations to all for the gutsy riding.
The 40 K time trial witnessed some record slow times due to the variability
in different car odometers. We apologize to anyone who was hoping to
accurately gage his or her year to year progress in this event. Even if the
distance wasn't quite perfect, the weather was certainly excellent. We
couldn't have asked for better weather over the entire weekend.
It was especially good to see the good turn out for the women's races.
Fifteen women raced hard for the medals, cash, bragging rights, conviviality,
and personal rewards that every bicycle racer lives for.
Here are the final results from each event.
2000 District Time Trial Championships (42 K)
Senior Men 1,2,3
1. John Weyrich 5VV 56:07
2. Josef Lemire GVBC 56:22
3. Jason Dykstra ECC 57:20 Cat 3
4. Geoff Proctor MT Velo 57:23
5. Fred Michini 5VV 58:06
6. Mark Legg 5VV 58:09
7. Andrew Michaelis 5VV 1:01:58 Cat 3
Women Category 2,3,4
1. Heidi Stamm 5VV 1:03:06 Cat 2
2. Eryn Hanna Gt Divide 1:10:23
3. Kelly Parsley Gt Divide 1:11:23
4. Sue Rydquist YVCC 1:15:08
5. Kristi Machler ECC 1:17:27
Junior 10-12
1. Tejay Van Garderen GVBC 15:47 (10 K)
Senior Men 4,5
1. Jeff Agamenoni ECC 1:03:25
2. Joel Freyenhagen ECC 1:06:35
Master 35-44
1. Greg Ebner Butte 1:01:31
2. John Juras ECC 1:03:39
3. Bruce Kramer 5VV 1:04:23
4. Robert Hunter YVCC 1:05:40
5. Jeff Olson YVCC 1:06:27
Master 45+
1. Tom Greason GVBC 56:46
2. Richard Allen 5VV 1:15:43
3. Ed Klein ECC 1:16:44
Master 60+
1. Edward Ancker GVBC 40:59 (20 K)
2000 District Road Race Championships
Senior Men 1,2,3 (104 Miles)
1. Mark Legg 5VV
2. Geoff Proctor MT Velo
3. John Weyrich 5VV
4. Jason Dykstra ECC Cat 3
5. Fred Michini 5VV
6. Josef Lemire GVBC
7. Owen Murphy Cat 3
8. Todd Struckman 5VV Cat 3
9. Wakefield Troy 5VV Cat 3
10. Jon Burkhartsmeyer Mt Velo
11. Andrew Michaelis 5VV Cat 3
Women Cat 2,3,4
1. Heidi Stamm 5VV Cat 2
2. Susan Gaunt 5VV Cat 3
3. Linda Biederman GVBC
4. Deborah Borden GVBC/Team WINS
5. Christine Wike Gt Divide
6. Margie Legg 5VV Cat 3
7. Linda Atwater GVBC/Team WINS
8. Kelly Parsley Gt Divide
9. Eryn Hanna Gt Divide
10. Darlene Crawford Ketchum ID
11. Tierra Villasenor MSU/SOBE
12. Rebecca Stanfel Helena
13. Sian Mooney GVBC/Team WINS
14. Sue Rydquist YVCC
Junior 10-12
1. Tejay Van Garderen GVBC
Junior 13-14
1. Mark Juras ECC
Senior Men 4,5
1. Jay Noonan FV
2. Tony Sabia Team Mack
3. Dave Hartman Mtn Goat
4. Will Schneider E Caps
5. Jeff Agamenoni ECC
6. Pete Lacny GVBC
7. Tom Robertson Mtn Goat
8. Jon Berman Northern Rockies
9. Bruce Robertson Mtn Goat
DNF Ric Heldt YVCC
Master 35-44
1. Eric Greenberg FV
2. John Juras ECC
3. Tim Gaertner FV
4. Rich West ECC
5. Joel Gerhart Gt Divide
6. Jon Engen GVBC
7. Jeff Olson YVCC
8. Bruce Kramer 5VV
9. John Coulthard Butte
Master 45+
1. Ken Toth FV
2. Tom Greason GVBC
3. Richard Allen 5VV


|
| | __init__ (const MouseWatcherRegion) |
| |
| | __init__ (str name, const LVecBase4 frame) |
| |
| | __init__ (str name, float left, float right, float bottom, float top) |
| |
| bool | getActive () |
| | Returns whether the region is active or not. More...
|
| |
| float | getArea () |
| | Returns the area of the rectangular region. More...
|
| |
| const LVecBase4 | getFrame () |
| |
| bool | getKeyboard () |
| | Returns whether the region is interested in global keyboard events; see set_keyboard(). More...
|
| |
| int | getSort () |
| | Returns the current sorting order of this region. More...
|
| |
| int | getSuppressFlags () |
| | Returns the current suppress_flags. More...
|
| |
| | output (Ostream out) |
| |
| | setActive (bool active) |
| | Sets whether the region is active or not. More...
|
| |
| | setFrame (const LVecBase4 frame) |
| |
| | setFrame (float left, float right, float bottom, float top) |
| |
| | setKeyboard (bool keyboard) |
| | Sets whether the region is interested in global keyboard events. More...
|
| |
| | setSort (int sort) |
| | Changes the sorting order of this particular region. More...
|
| |
| | setSuppressFlags (int suppress_flags) |
| | Sets which events are suppressed when the mouse is over the region. More...
|
| |
| | write (Ostream out, int indent_level) |
| |
| Public Member Functions inherited from TypedWritable |
| object | __reduce__ () |
| |
| object | __reduce_persist__ (object pickler) |
| |
| VectorUchar | encodeToBamStream () |
| | Converts the TypedWritable object into a single stream of data using a BamWriter, and returns that data as a bytes object. More...
|
| |
| bool | encodeToBamStream (VectorUchar data, BamWriter writer) |
| | Converts the TypedWritable object into a single stream of data using a BamWriter, and stores that data in the indicated string. More...
|
| |
| | fillin (DatagramIterator scan, BamReader manager) |
| | This internal function is intended to be called by each class's make_from_bam() method to read in all of the relevant data from the BamFile for the new object. More...
|
| |
| UpdateSeq | getBamModified () |
| | Returns the current bam_modified counter. More...
|
| |
| | markBamModified () |
| | Increments the bam_modified counter, so that this object will be invalidated and retransmitted on any open bam streams. More...
|
| |
| Public Member Functions inherited from TypedObject |
| TypeHandle | getType () |
| |
| int | getTypeIndex () |
| | Returns the internal index number associated with this object's TypeHandle, a unique number for each different type. More...
|
| |
| bool | isExactType (TypeHandle handle) |
| | Returns true if the current object is the indicated type exactly. More...
|
| |
| bool | isOfType (TypeHandle handle) |
| | Returns true if the current object is or derives from the indicated type. More...
|
| |
| Public Member Functions inherited from ReferenceCount |
| int | getRefCount () |
| | Returns the current reference count. More...
|
| |
| | ref () |
| | Explicitly increments the reference count. More...
|
| |
| bool | testRefCountIntegrity () |
| | Does some easy checks to make sure that the reference count isn't completely bogus. More...
|
| |
| bool | testRefCountNonzero () |
| | Does some easy checks to make sure that the reference count isn't zero, or completely bogus. More...
|
| |
| bool | unref () |
| | Explicitly decrements the reference count. More...
|
| |
| Public Member Functions inherited from Namable |
| | __init__ (const Namable) |
| |
| | __init__ (str initial_name) |
| |
| | clearName () |
| | Resets the Namable's name to empty. More...
|
| |
| str | getName () |
| |
| bool | hasName () |
| | Returns true if the Namable has a nonempty name set, false if the name is empty. More...
|
| |
| | output (Ostream out) |
| | Outputs the Namable. More...
|
| |
| | setName (str name) |
| |
This is the class that defines a rectangular region on the screen for the MouseWatcher.
Changes the sorting order of this particular region.
The sorting order is used to resolve conflicts in the case of overlapping region; the region with the highest sort value will be preferred, and between regions of the same sort value, the smallest region will be preferred. The default sorting order, if none is explicitly specified, is 0.
| setSuppressFlags |
( |
int |
suppress_flags | ) |
|
Sets which events are suppressed when the mouse is over the region.
This is the union of zero or more various SF_* values. Normally, this is 0, indicating that no events are suppressed.
If you set this to a non-zero value, for instance SF_mouse_position, then the mouse position will not be sent along the data graph when the mouse is over this particular region.


{% extends 'base.html' %} {% block header %}
A simple application skeleton for the so-called Knowledge Graph projects.
Show your data to users & Talk about your success.
Have a TrySource Code →


Tech Valley Ruby Brigade
Events
Users
Projects
Jobs
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404 –
file not found
But! We did find this...


package main
import (
"bufio"
"fmt"
"os"
"strconv"
"strings"
)
func main() {
reader := bufio.NewReader(os.Stdin)
var digit int
var reverse int
fmt.Println("Enter a number:")
number, _ := reader.ReadString('\n')
number = strings.TrimSpace(number)
intNumber, _ := strconv.Atoi(number)
userNumber := intNumber
fmt.Println("Number enter by user",intNumber)
for intNumber > 0 {
digit = intNumber % 10
reverse = reverse*10 + digit
intNumber = intNumber / 10
fmt.Println(digit, reverse, intNumber)
}
if userNumber == reverse {
fmt.Println(userNumber, "is palindrome.")
} else {
fmt.Println(userNumber, "is not palindrome.")
}
}
Enter a number:
2002
Number enter by user 2002
2 2 200
0 20 20
0 200 2
2 2002 0
2002 is palindrome.
In this program, we take an integer number from user as an input value.
As an output we reverse the number
and check whether a number is palindrome or not.


SNOBOL4EXT(1) | CSNOBOL4B 2.0 | January 1, 2015
NAME
snobol4ext – CSNOBOL4 extensions
DESCRIPTION
This page describes extensions to the Bell Telephone Laboratories
portable implementation of SNOBOL4 in snobol4(1).
-
ARRAY/TABLE access
-
Multiple ARRAY and/or TABLE index operations may appear in a
row, without having to resort to use of the ELEMENT function, so
long as no intervening spaces (or line continuations) appear.
-
Case folding
-
By default the compiler folds identifiers and directives to upper
case, so programs can be entered in either case:
see snobol4(1),
-CASE in snobol4ctrl(1),
&CASE in snobol4key(1).
-
Control lines
-
The following additional “control lines” are implemented:
-CASE,
-COPY,
-ERROR,
-NOERRORS,
-EXECUTE,
-NOEXECUTE,
-HIDE,
-INCLUDE,
-LINE:
See snobol4ctrl(1).
-
Functions
-
The following additional functions are implemented:
ATAN(),
BREAKX(),
CHAR(),
CHOP()
COS(),
DATE(),
DELETE(),
EXP(),
FILE(),
FILE_ABSPATH(),
FILE_ISDIR()
FREEZE(),
FUNCTION(),
HOST(),
IO_FINDUNIT()
LABEL(),
LEQ(),
LGE(),
LLE(),
LLT(),
LN(),
LNE(),
LOG(),
LPAD(),
ORD(),
RENAME(),
REVERSE(),
RPAD(),
RSORT(),
SET()
SETEXIT(),
SIN(),
SORT(),
SQRT(),
SSET(),
SUBSTR(),
TAN(),
THAW(),
VDIFFER():
see snobol4func(1).
-
Keywords
-
The following additional keywords are implemented:
&CASE,
&DIGITS,
&ERRTEXT
&FILE,
&GCTIME,
>RACE
&LASTFILE,
&LASTLINE,
&LCASE,
&LINE,
&MAXINT,
&PARM,
&PI,
&STEXEC,
&UCASE:
see snobol4key(1).
-
REAL numbers in INTEGER contexts
-
REAL numbers (or strings convertible to REAL) are accepted in
all contexts which previously required an INTEGER (or string
convertible to INTEGER). Contexts include TABLE(), ITEM(),
array indices, INPUT(), OUTPUT(), SET(), keyword values,
CHAR(), RPAD(), LPAD(), FIELD(), COLLECT(), DUMP(),
DUPL(), OPSYN(), SUBSTR().
-
Scientific notation
-
REAL number syntax has been expanded to allow exponents of the
form: ANY('Ee') ('+' | '-' | ”) SPAN('0123456789'). Exponential
format reals need not contain a decimal point.
-
SPITBOL operators
-
The SPITBOL scan (?) and assignment (=) operators have been added. A
pattern match can appear within an expression, and returns the matched
string as its value. Similarly assignment can appear in an
expression, and returns the assigned value. An assignment after a scan
(ie; STRING ? PATTERN = VALUE) performs a scan and replace.
Assignment is right associative, and has the lowest precedence, while
scan is left associative and has a precedence just higher than
assignment.
-
-
The SPITBOL selection/alternative construction can be used in any
expression. It consists of a comma separated list of expressions
inside parentheses. The expressions are evaluated until one succeeds,
and its value is returned. Abuse of this construction may result in
incomprehensible code.
-
-
The type NUMERIC with CONVERT() and the removal of leading
spaces from strings converted to numbers (implicitly or explicitly)
are also legal when SPITBOL extensions are enabled. SPITBOL
extensions can be enabled and disabled using the -PLUSOPS
directive. -PLUSOPS 0 or -PLUSOPS disables SPITBOL operators,
while -PLUSOPS n where n is a non-zero integer enables them.
SPITBOL extensions are enabled by default.
-
TERMINAL I/O variable
-
The variable TERMINAL is associated with the standard error file
descriptor for both input and output.
-
TRACE() function type argument
-
The second argument of the TRACE() function can be abbreviated to a
single letter:
C (CALL),
F (FUNCTION),
K (KEYWORD),
L (LABEL),
R (RETURN), or
V (VALUE) as in Macro SPITBOL.
-
TRACE() of additional keywords
-
&ERRTEXT and &STFCOUNT can be traced.
-
Value TRACE() on pattern match assignments
-
variable changes by . and $ operators are visible via value trace.
BUGS
A lot of stuff still needs to move to other pages!
Group the keywords, control lines, functions, operators together!
SEE ALSO
snobol4(1).
snobol4ctrl(1),
snobol4func(1),
snobol4key(1).


 You can always see or remove access in your
ASII account.
Find out about more the risks.


JobShopBase Class Reference
[Example scripts (models)]
Example: Job Shop Scheduling More...
Public Member Functions | |
| JobShopBase (const JobShopOptions &o) | |
| Actual model. | |
| void | nooverload (void) |
| Do not overload machines. | |
| virtual IntVar | cost (void) const |
| Return cost. | |
| JobShopBase (JobShopBase &s) | |
| Constructor for cloning s. | |
| virtual void | print (std::ostream &os) const |
| Print solution. | |
Protected Attributes | |
| const JobShopOptions & | opt |
| Options. | |
| const Spec | spec |
| Specification. | |
| IntVarArray | start |
| Start times for each step in a job. | |
| IntVar | makespan |
| Makespan. |
Detailed Description
Example: Job Shop Scheduling
Definition at line 268 of file job-shop.cpp.
Constructor & Destructor Documentation
| JobShopBase::JobShopBase | ( | const JobShopOptions & | o | ) | [inline] |
Actual model.
Definition at line 280 of file job-shop.cpp.
| JobShopBase::JobShopBase | ( | JobShopBase & | s | ) | [inline] |
Constructor for cloning s.
Definition at line 324 of file job-shop.cpp.
Member Function Documentation
| void JobShopBase::nooverload | ( | void | ) | [inline] |
Do not overload machines.
Definition at line 297 of file job-shop.cpp.
| virtual IntVar JobShopBase::cost | ( | void | ) | const [inline, virtual] |
Return cost.
Definition at line 320 of file job-shop.cpp.
| virtual void JobShopBase::print | ( | std::ostream & | os | ) | const [inline, virtual] |
Print solution.
Reimplemented from Gecode::Driver::ScriptBase< BaseSpace >.
Definition at line 331 of file job-shop.cpp.
Member Data Documentation
const JobShopOptions& JobShopBase::opt [protected] |
Options.
Definition at line 271 of file job-shop.cpp.
const Spec JobShopBase::spec [protected] |
Specification.
Definition at line 273 of file job-shop.cpp.
IntVarArray JobShopBase::start [protected] |
Start times for each step in a job.
Definition at line 275 of file job-shop.cpp.
IntVar JobShopBase::makespan [protected] |
Makespan.
Definition at line 277 of file job-shop.cpp.
The documentation for this class was generated from the following file:
- examples/job-shop.cpp


Resistor Calculator
- Resistor color code calculator - 3, 4 and 5 band resistors
- Resistor color code calculator - 4, 5 and 6 band resistors
Referance Materials
Purchasing Sites
- New Product
- On The Web
- Pololu Robotics and Electronics
- MDFLY Electronics
- Sparkfun Electronics
- Digi-Key Electronics
- Adafruit Industries
- Jameco Electronics
- Mouser Electronics
- Parallax Inc
- Seeed
- Solutions Cubed Products
- Electrodragon
- LowPowerLab
- EpicTinker
- Stock Drive Product / Sterling Instrument - SDP/SI
- Amphenol Cables on Demand
- Monoprice
- Pasternack RF and Microwave Products
- newegg.com
- HobbyKing
- McMaster-Carr - Fastening, Joining, Gears
- Inventables
- Hobby Wireless
- Vernier - afforable scientific data collection
- NooElec
- Phidgets
- Local Stores
- On The Web
- Used / Surplus Product
Training Materials
- Software Tutorials
- Hardware Tuitorals
- Radio Electronics
Prototype Boards
Lists From Other Blogs
This is an ongoing growing list of links for beginners in electronics written by Todd Harrison. * Links For Beginners In Electronics And Engineering


Hello, world!
Welcome to my portfolio.
Below is a list of projects I have created with active links assigned to them.
I was born in 1997 in Kericho,a county in Kenya known as the green town.I started learning in the year 2000.I finished primary school in 2010 and proceeded to high school upto the year 2014.I joined Kisii university and completed studying Business Information Technology in 2017 while working with Huduma Centre-Kericho.Currently I am undergoing programming at Moringa School.
Since I joined university I had the intrest in developing softwares that led me to start programming courses at Moringa School.
Below is a list of my hobbies,


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 8. Data Types | Next |
Numeric types consist of two-, four-, and eight-byte integers, four- and eight-byte floating-point numbers, and selectable-precision decimals. Table 8-2 lists the available types.
Table 8-2. Numeric Types
| Name | Storage Size | Description | Range |
|---|---|---|---|
| smallint | 2 bytes | small-range integer | -32768 to +32767 |
| integer | 4 bytes | typical choice for integer | -2147483648 to +2147483647 |
| bigint | 8 bytes | large-range integer | -9223372036854775808 to<PHONE_NUMBER>854775807 |
| decimal | variable | user-specified precision, exact | no limit |
| numeric | variable | user-specified precision, exact | no limit |
| real | 4 bytes | variable-precision, inexact | 6 decimal digits precision |
| double precision | 8 bytes | variable-precision, inexact | 15 decimal digits precision |
| serial | 4 bytes | autoincrementing integer | 1 to<PHONE_NUMBER> |
| bigserial | 8 bytes | large autoincrementing integer | 1 to<PHONE_NUMBER>854775807 |
The syntax of constants for the numeric types is described in Section 4.1.2. The numeric types have a full set of corresponding arithmetic operators and functions. Refer to Chapter 9 for more information. The following sections describe the types in detail.
The types smallint, integer, and bigint store whole numbers, that is, numbers without fractional components, of various ranges. Attempts to store values outside of the allowed range will result in an error.
The type integer is the common choice, as it offers the best balance between range, storage size, and performance. The smallint type is generally only used if disk space is at a premium. The bigint type should only be used if the integer range is insufficient, because the latter is definitely faster.
On very minimal operating systems the bigint type might not function correctly, because it relies on compiler support for eight-byte integers. On such machines, bigint acts the same as integer, but still takes up eight bytes of storage. (We are not aware of any modern platform where this is the case.)
SQL only specifies the integer types integer (or int), smallint, and bigint. The type names int2, int4, and int8 are extensions, which are also used by some other SQL database systems.
The type numeric can store numbers with up to 1000 digits of precision and perform calculations exactly. It is especially recommended for storing monetary amounts and other quantities where exactness is required. However, arithmetic on numeric values is very slow compared to the integer types, or to the floating-point types described in the next section.
We use the following terms below: The scale of a numeric is the count of decimal digits in the fractional part, to the right of the decimal point. The precision of a numeric is the total count of significant digits in the whole number, that is, the number of digits to both sides of the decimal point. So the number 23.5141 has a precision of 6 and a scale of 4. Integers can be considered to have a scale of zero.
Both the maximum precision and the maximum scale of a numeric column can be configured. To declare a column of type numeric use the syntax:
NUMERIC(precision, scale)
The precision must be positive, the scale zero or positive. Alternatively:
NUMERIC(precision)
selects a scale of 0. Specifying:
NUMERIC
without any precision or scale creates a column in which numeric values of any precision and scale can be stored, up to the implementation limit on precision. A column of this kind will not coerce input values to any particular scale, whereas numeric columns with a declared scale will coerce input values to that scale. (The SQL standard requires a default scale of 0, i.e., coercion to integer precision. We find this a bit useless. If you're concerned about portability, always specify the precision and scale explicitly.)
If the scale of a value to be stored is greater than the declared scale of the column, the system will round the value to the specified number of fractional digits. Then, if the number of digits to the left of the decimal point exceeds the declared precision minus the declared scale, an error is raised.
Numeric values are physically stored without any extra leading or trailing zeroes. Thus, the declared precision and scale of a column are maximums, not fixed allocations. (In this sense the numeric type is more akin to varchar(n) than to char(n).) The actual storage requirement is two bytes for each group of four decimal digits, plus five to eight bytes overhead.
In addition to ordinary numeric values, the numeric type allows the special value NaN, meaning "not-a-number". Any operation on NaN yields another NaN. When writing this value as a constant in an SQL command, you must put quotes around it, for example UPDATE table SET x = 'NaN'. On input, the string NaN is recognized in a case-insensitive manner.
Note: In most implementations of the "not-a-number" concept, NaN is not considered equal to any other numeric value (including NaN). In order to allow numeric values to be sorted and used in tree-based indexes, PostgreSQL treats NaN values as equal, and greater than all non-NaN values.
The types decimal and numeric are equivalent. Both types are part of the SQL standard.
The data types real and double precision are inexact, variable-precision numeric types. In practice, these types are usually implementations of IEEE Standard 754 for Binary Floating-Point Arithmetic (single and double precision, respectively), to the extent that the underlying processor, operating system, and compiler support it.
Inexact means that some values cannot be converted exactly to the internal format and are stored as approximations, so that storing and retrieving a value might show slight discrepancies. Managing these errors and how they propagate through calculations is the subject of an entire branch of mathematics and computer science and will not be discussed here, except for the following points:
If you require exact storage and calculations (such as for monetary amounts), use the numeric type instead.
If you want to do complicated calculations with these types for anything important, especially if you rely on certain behavior in boundary cases (infinity, underflow), you should evaluate the implementation carefully.
Comparing two floating-point values for equality might not always work as expected.
On most platforms, the real type has a range of at least 1E-37 to 1E+37 with a precision of at least 6 decimal digits. The double precision type typically has a range of around 1E-307 to 1E+308 with a precision of at least 15 digits.  Rounding might take place if the precision of an input number is too high. Numbers too close to zero that are not representable as distinct from zero will cause an underflow error.
In addition to ordinary numeric values, the floating-point types have several special values:
Infinity
-Infinity
NaN
Note: IEEE754 specifies that NaN should not compare equal to any other floating-point value (including NaN). In order to allow floating-point values to be sorted and used in tree-based indexes, PostgreSQL treats NaN values as equal, and greater than all non-NaN values.
PostgreSQL also supports the SQL-standard notations float and float(p) for specifying inexact numeric types. Here, p specifies the minimum acceptable precision in binary digits. PostgreSQL accepts float(1) to float(24) as selecting the real type, while float(25) to float(53) select double precision. Values of p outside the allowed range draw an error. float with no precision specified is taken to mean double precision.
Note: Prior to PostgreSQL 7.4, the precision in float(p) was taken to mean so many decimal digits. This has been corrected to match the SQL standard, which specifies that the precision is measured in binary digits. The assumption that real and double precision have exactly 24 and 53 bits in the mantissa respectively is correct for IEEE-standard floating point implementations. On non-IEEE platforms it might be off a little, but for simplicity the same ranges of p are used on all platforms.
The data types serial and bigserial are not true types, but merely a notational convenience for creating unique identifier columns (similar to the AUTO_INCREMENT property supported by some other databases). In the current implementation, specifying:
CREATE TABLE tablename ( colname SERIAL );
is equivalent to specifying:
CREATE SEQUENCE tablename_colname_seq; CREATE TABLE tablename ( colname integer NOT NULL DEFAULT nextval('tablename_colname_seq') ); ALTER SEQUENCE tablename_colname_seq OWNED BY tablename.colname;
Thus, we have created an integer column and arranged for its default values to be assigned from a sequence generator. A NOT NULL constraint is applied to ensure that a null value cannot be inserted. (In most cases you would also want to attach a UNIQUE or PRIMARY KEY constraint to prevent duplicate values from being inserted by accident, but this is not automatic.) Lastly, the sequence is marked as "owned by" the column, so that it will be dropped if the column or table is dropped.
Note: Prior to PostgreSQL 7.3, serial implied UNIQUE. This is no longer automatic. If you wish a serial column to have a unique constraint or be a primary key, it must now be specified, just like any other data type.
To insert the next value of the sequence into the serial column, specify that the serial column should be assigned its default value. This can be done either by excluding the column from the list of columns in the INSERT statement, or through the use of the DEFAULT key word.
The type names serial and serial4 are equivalent: both create integer columns. The type names bigserial and serial8 work the same way, except that they create a bigint column. bigserial should be used if you anticipate the use of more than 231 identifiers over the lifetime of the table.
The sequence created for a serial column is automatically dropped when the owning column is dropped. You can drop the sequence without dropping the column, but this will force removal of the column default expression.